Changing role of water table and weather conditions in diameter growth of Scots pine in drained peatlands
Bibliographic record
Abstract
We investigated the impact of water table (WT), monthly temperature, and precipitation of current and previous growing seasons on annual diameter growth of Scots pine in boreal drained peatlands. The data were collected from six sites across Finland. WT was monitored during 5–8 growing seasons depending on site during 2007–2014. The sites contained altogether 19 sample plots, where diameter growth from 339 trees was measured, resulting in 1599 growth observations. Tree-level diameter growth was analysed using mixed linear models, including variables describing tree size, competition, and environment. The higher precipitation in June of the previous year and May of the current year increased diameter growth, whereas the higher temperature in July of the current year and the deeper WT in August of the previous year decreased growth. The results suggest that Scots pine grows better at shallow than deep WT in drained peatlands. This contradicts earlier findings that a deep WT is needed to support tree growth in drained peatlands. We suggest that the development of a mor layer on the peat is changing nutrient cycling and hydrology. The results encourage the avoidance of intensive drainage in forested peatlands, which may also diminish the adverse environmental impacts of peatland forestry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".